tbuckley/cabot-search huggingface dataset:
AI‐Driven Design System for Fabrication of Inhalable Nanocatchers for Virus Capture and Neutralization
Authors: Hao‐Han Pang, Nan‐Si Li, Ying‐Pei Hsu, Shin‐Pon Ju, Guan‐Da Syu, Pin‐Xian Du, Chiung‐Yin Huang, Kuo‐Chen Wei, Hung‐Wei Yang
Journal: Advanced Healthcare Materials (2023)

The global pandemic presents a critical threat to humanity, with no effective rapid-response solutions for early-stage virus dissemination. This study aims to create an AI-driven entry-blocker design system (AIEB) to fabricate inhalable virus-like nanocatchers (VLNCs) fused with entry-blocking peptides (EBPs) to counter pandemic viruses and explore therapeutic applications. This work focuses on developing angiotensin-converting enzyme 2 (ACE2)-mimic domain-fused VLNCs (ACE2@VLNCs) using AIEB and analyzing their interaction with the SARS-CoV-2 receptor binding domain (RBD), demonstrating their potential to hinder SARS-CoV-2 infection. Aerosol-based tests show ACE2@VLNCs persist over 70 min in the air and neutralize pseudoviruses within 30 min, indicating their utility in reducing airborne virus transmission. In vivo results reveal ACE2@VLNCs mitigate over 67% of SARS-CoV-2 infections. Biosafety studies confirm their safety, causing no damage to eyes, skin, lungs, or trachea, and not eliciting significant immune responses. These findings offer crucial insights into pandemic virus prevention and treatment, highlighting the potential of the ACE2@VLNCs system as a promising strategy against future pandemics.
Quantifying Sharpness and Nonlinearity in Neonatal Seizure Dynamics
Authors: Chien-Hung Yeh, Chuting Zhang, Wenbin Shi, Boyi Zhang, Jianping An
Journal: Cyborg and Bionic Systems (2023)

The integration of multiple electrophysiological biomarkers is crucial for monitoring neonatal seizure dynamics. The present study aimed to characterize the temporal dynamics of neonatal seizures by analyzing intrinsic waveforms of epileptic electroencephalogram (EEG) signals. We proposed a complementary set of methods considering envelope power, focal sharpness changes, and nonlinear patterns of EEG signals of 79 neonates with seizures. Features derived from EEG signals were used as input to the machine learning classifier. All three characteristics were significantly elevated during seizure events, as agreed upon by all viewers (
Developing Liver Microphysiological Systems for Biomedical Applications
Authors: Jinglin Wang, Xiangyi Wu, Junqi Zhao, Haozhen Ren, Yuanjin Zhao
Journal: Advanced Healthcare Materials (2023)

Microphysiological systems (MPSs), also known as organ chips, are micro-units that integrate cells with diverse physical and biochemical environmental cues. In the field of liver MPSs, cellular components have advanced from simple planar cell cultures to more sophisticated 3D formations such as spheroids and organoids. Additionally, progress in microfluidic devices, bioprinting, engineering of matrix materials, and interdisciplinary technologies have significant promise for producing MPSs with biomimetic structures and functions. This review provides a comprehensive summary of biomimetic liver MPSs including their clinical applications and future developmental potential. First, the key components of liver MPSs, including the principal cell types and engineered structures utilized for cell cultivation, are briefly introduced. Subsequently, the biomedical applications of liver MPSs, including the creation of disease models, drug absorption, distribution, metabolism, excretion, and toxicity, are discussed. Finally, the challenges encountered by MPSs are summarized, and future research directions for their development are proposed.
Self‐Assembled Multiepitope Nanovaccine Provides Long‐Lasting Cross‐Protection against Influenza Virus
Authors: Jiaojiao Nie, Qingyu Wang, Chenxi Li, Yongfei Zhou, Xin Yao, Lipeng Xu, Yaotian Chang, Fan Ding, Lulu Sun, Li Zhan, Lvzhou Zhu, Kunpeng Xie, Xu Wang, Yuhua Shi, Qi Zhao, Yaming Shan
Journal: Advanced Healthcare Materials (2023)

Abstract Seasonal influenza vaccines typically provide strain‐specific protection and are reformulated annually, which is a complex and time‐consuming process. Multiepitope vaccines, combining multiple conserved antigenic epitopes from a pathogen, can trigger more robust, diverse, and effective immune responses, providing a potential solution. However, their practical application is hindered by low immunogenicity and short‐term effectiveness. In this study, multiple linear epitopes from the conserved stem domain of hemagglutinin and the ectodomain of matrix protein 2 are combined with the Helicobacter pylori ferritin, a stable self‐assembled nanoplatform, to develop an influenza multiepitope nanovaccine, named MHF. MHF is prokaryotically expressed in a soluble form and self‐assembles into uniform nanoparticles. The subcutaneous immunization of mice with adjuvanted MHF induces cross‐reactive neutralizing antibodies, antibody‐dependent cell‐mediated cytotoxicity, and cellular immunity, offering complete protection against H3N2 as well as partial protection against H1N1. Importantly, the vaccine cargo delivered by ferritin triggers epitope‐specific memory B‐cell responses, with antibody level persisting for over 6 months post‐immunization. These findings indicate that self‐assembled multiepitope nanovaccines elicit potent and long‐lasting immune responses while significantly reducing the risk of vaccine escape mutants, and offer greater practicality in terms of scalable manufacturing and genetic manipulability, presenting a promising and effective strategy for future vaccine development.
Recent Advances on NIR‐II Light‐Enhanced Chemodynamic Therapy
Authors: Gui‐long Wu, Xiaofeng Tan, Qinglai Yang
Journal: Advanced Healthcare Materials (2023)

Abstract Chemodynamic therapy (CDT) is a particular oncological therapeutic strategy by generates the highly toxic hydroxyl radical (•OH) from the dismutation of endogenous hydrogen peroxide (H 2 O 2 ) via Fenton or Fenton‐like reactions. However, single CDT therapies have been limited by unsatisfactory efficacy. Enhanced chemodynamic therapy (ECDT) triggered by near‐infrared (NIR) is a novel therapeutic modality based on light energy to improve the efficiency of Fenton or Fenton‐like reactions. However, the limited penetration and imaging capability of the visible (400–650 nm) and traditional NIR‐I region (650–900 nm) light‐amplified CDT restrict the prospects for its clinical application. Combined with the high penetration/high precision imaging characteristics of the second near‐infrared (NIR‐II,) nanoplatform, it is expected to kill deep tumors efficiently while imaging the treatment process in real‐time, and more notably, the NIR‐II region radiation with wavelengths above 1000 nm can minimize the irradiation damage to normal tissues. Such NIR‐II ECDT nanoplatforms have greatly improved the effectiveness of CDT therapy and demonstrated extraordinary potential for clinical applications. Accordingly, various strategies have been explored in the past years to improve the efficiency of NIR‐II Enhanced CDT. In this review, the mechanisms and strategies used to improve the performance of NIR‐II‐enhanced CDT are outlined.
Considerations for collecting and analyzing longitudinal data in observational cohort studies of transgender, non-binary, and gender diverse people
Authors: S. Bailey, N. Newton, Y. Perry, L. Grummitt, S. Smout, E. Barrett
Journal: International Journal of Transgender Health (2023)

The health and well-being of transgender, non-binary, and gender-diverse people is receiving increasing attention from epidemiologists and public health researchers, including those utilizing longitudinal observational cohort studies. These longitudinal studies are advantageous over cross-sectional observational study designs given their scope over several timepoints rather than one, and when exposures and outcomes are prospectively measured this improves validity of causal claims. However, within these longitudinal studies, gender is often collected inconsistently (e.g. only asked at a single timepoint), or inadequately (e.g. questions that use limiting notions of gender). Due to the temporal nature of gender, this introduces potential including misclassification error and may provide an incomplete picture of gender diversity in a sample. This article considers these methodological issues and offers evidence-based recommendations to ensure longitudinal data on trans, non-binary, and gender-diverse people is treated with epidemiological rigor, while maintaining inclusivity.
Genitourinary cancers updates: highlights from ASCO 2023
Authors: Qian Qin, Hollie Sheffield, Sean M. Taasan, Andrew Z. Wang, Tian Zhang
Journal: Journal of Hematology & Oncology (2023)

Abstract Significant scientific advances in immunotherapy and targeted therapy approaches have improved clinical outcomes and increased treatment options for patients with genitourinary (GU) malignancies. We highlight the clinical trial developments released at the ASCO 2023 annual meeting, including PARP inhibitors for prostate cancer, antibody drug conjugates and fibroblast growth factor receptor inhibitors for urothelial cancer, and HIF2a inhibitors for renal cell carcinoma. Novel agents such as bispecific antibodies, chimeric antigen receptor T-cells, and radiopharmaceuticals are currently in early phase development and also have high potential impact for the GU cancer landscape. With more treatment options, the field will need to define best treatment sequencing to optimize outcomes for each patient.
Residual Aligner-based Network (RAN): Motion-separable structure for coarse-to-fine discontinuous deformable registration
Authors: Jian-Qing Zheng, Ziyang Wang, Baoru Huang, Ngee Han Lim, Bartłomiej W. Papież
Journal: Medical Image Analysis (2023)

Deformable image registration, the estimation of the spatial transformation between different images, is an important task in medical imaging. Deep learning techniques have been shown to perform 3D image registration efficiently. However, current registration strategies often only focus on the deformation smoothness, which leads to the ignorance of complicated motion patterns (e.g., separate or sliding motions), especially for the intersection of organs. Thus, the performance when dealing with the discontinuous motions of multiple nearby objects is limited, causing undesired predictive outcomes in clinical usage, such as misidentification and mislocalization of lesions or other abnormalities. Consequently, we proposed a novel registration method to address this issue: a new Motion Separable backbone is exploited to capture the separate motion, with a theoretical analysis of the upper bound of the motions' discontinuity provided. In addition, a novel Residual Aligner module was used to disentangle and refine the predicted motions across the multiple neighboring objects/organs. We evaluate our method, Residual Aligner-based Network (RAN), on abdominal Computed Tomography (CT) scans and it has shown to achieve one of the most accurate unsupervised inter-subject registration for the 9 organs, with the highest-ranked registration of the veins (Dice Similarity Coefficient (%)/Average surface distance (mm): 62%/4.9mm for the vena cava and 34%/7.9mm for the portal and splenic vein), with a smaller model structure and less computation compared to state-of-the-art methods. Furthermore, when applied to lung CT, the RAN achieves comparable results to the best-ranked networks (94%/3.0mm), also with fewer parameters and less computation.
Delays between Uveal Melanoma Diagnosis and Treatment Increase the Risk of Metastatic Death
Authors: Gustav Stålhammar
Journal: Ophthalmology (2023)

PurposeTo investigate if the interval between diagnosis and treatment of posterior uveal melanoma is associated with metastatic death.DesignRetrospective, single-center cohort study.Subjects1145 patients consecutively diagnosed with posterior uveal melanoma at St. Erik Eye Hospital, Stockholm, from 2012 to 2022, with recorded dates of diagnosis and primary treatment. This cohort represents 81% of all diagnosed patients in Sweden during this period.MethodsData on the interval between diagnosis and treatment were collected for all patients. Its prognostic importance was examined with uni- and multivariate competing risks regressions, and with cumulative incidence analyses.Main Outcome MeasuresIncidence of metastatic death (UM mortality) for patients with prompt (<one month from diagnosis) vs. delayed treatment (≥one month), and subdistribution hazard ratios (exp(βj)) for every additional 10-day delay in treatment.ResultsThe mean interval between diagnosis and treatment was 34 days (SD 56, range 0–932). Patients treated promptly had larger tumors, but there were no differences in patient age at diagnosis, tumor distance to the optic disc, in rates of ciliary body involvement (CIB) or extraocular extension (EXE), or in symptom duration before diagnosis. Those who were treated more than a month after diagnosis had greater UM mortality in American Joint Committee on Cancer (AJCC) stage II and III. In stage I, the UM mortality for delayed treatment was lower for the first 10 years, followed by a marked spike in the 11th year. In multivariate competing risks regressions of all 1145 patients with tumor diameter, thickness, CIB and EXE as covariates, the risk for UM mortality increased with 1% for every additional 10-day delay in treatment (exp(βj) 1.01). Among 355 patients treated with enucleation, this delay was associated with UM mortality, independent of AJCC stage, cytomorphology, and level of immunohistochemical BAP-1 expression.ConclusionsIncreasing time between diagnosis and treatment of uveal melanoma is associated with a higher risk of metastatic death. These results challenge a central concept in the understanding of metastatic progression and may indicate the existence of late metastatic seeding. They also underscore the importance of prompt treatment. Validation in independent cohorts is recommended.
Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program
Authors: Ming-Ying Lu, Chung-Feng Huang, Chao-Hung Hung, Chi‐Ming Tai, Lein-Ray Mo, Hsing-Tao Kuo, Kuo-Chih Tseng, Ching-Chu Lo, Ming-Jong Bair, Szu-Jen Wang, Jee-Fu Huang, Ming-Lun Yeh, Chun-Ting Chen, Ming-Chang Tsai, Chien-Wei Huang, Pei-Lun Lee, Tzeng-Hue Yang, Yi-Hsiang Huang, Lee-Won Chong, Chien-Lin Chen, Chi-Chieh Yang, Sheng‐Shun Yang, Pin-Nan Cheng, Tsai-Yuan Hsieh, Jui-Ting Hu, Wen-Chih Wu, Chien-Yu Cheng, Guei-Ying Chen, Guo-Xiong Zhou, Wei-Lun Tsai, Chien-Neng Kao, Chih-Lang Lin, Chia-Chi Wang, Ta-Ya Lin, Chih‐Lin Lin, Wei-Wen Su, Tzong-Hsi Lee, Te-Sheng Chang, Chun-Jen Liu, Chia-Yen Dai, Jia-Horng Kao, Han-Chieh Lin, Wan-Long Chuang, Cheng-Yuan Peng, Chun-Wei- Tsai, Chi-Yi Chen, Ming-Lung Yu
Journal: Clinical and Molecular Hepatology (2023)

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy.Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset.Conclusions: Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.
MiR126-targeted-nanoparticles combined with PI3K/AKT inhibitor as a new strategy to overcome melanoma resistance
Authors: Maria Beatrice Arasi, Gabriele De Luca, Laura Chronopoulou, Francesca Pedini, Eleonora Petrucci, Michela Flego, Annarita Stringaro, Marisa Colone, Luca Pasquini, Massimo Spada, Valentina Lulli, Maria Chiara Perrotta, George Adrian Calin, Cleofe Palocci, Mauro Biffoni, Federica Felicetti, Nadia Felli
Journal: Molecular Therapy (2023)

Metastatic melanoma poses significant challenges as a highly lethal disease. Despite the success of molecular targeting using BRAFV600E inhibitors (BRAFi) and immunotherapy, the emergence of early recurrence remains an issue and there is the need for novel therapeutic approaches. This study aimed at creating a targeted delivery system for the oncosuppressor miR126 and testing its effectiveness in combination with a PI3K/AKT inhibitor for treating metastatic melanoma resistant to BRAFi. To achieve this, we synthesized chitosan nanoparticles containing a chemically modified miR126 sequence. These nanoparticles were further functionalized with an antibody specific to the chondroitin sulfate proteoglycan 4 (CSPG4) melanoma marker. After evaluation in vitro, the efficacy of this treatment was evaluated through an in vivo experiment using mice bearing resistant human melanoma. The co-administration of miR126 and the PI3K/AKT inhibitor in these experiments significantly reduced tumor growth and inhibited the formation of liver and lung metastases. These results provide evidence for a strategy to target an oncosuppressive nucleic acid sequence to tumor cells while simultaneously protecting it from plasma degradation. The system described in this study exhibits encouraging potential for the effective treatment of therapy-resistant metastatic melanoma, while also presenting a prospective approach for other forms of cancer.
A Liver Stiffness–Based Etiology-Independent Machine Learning Algorithm to Predict Hepatocellular Carcinoma
Authors: Huapeng Lin, Guanlin Li, Adèle Delamarre, Sang Hoon Ahn, Xinrong Zhang, Beom Kyung Kim, Lilian Yan Liang, Hye Won Lee, Grace Lai-Hung Wong, Pong-Chi Yuen, Henry Lik-Yuen Chan, Stephen Lam Chan, Vincent Wai-Sun Wong, Victor de Lédinghen, Seung Up Kim, Terry Cheuk-Fung Yip
Journal: Clinical Gastroenterology and Hepatology (2023)
Efficacy of SIGMAR1-based therapy in the early treatment of confirmed mild symptomatic COVID-19 patients
Authors: Santiago Grau, José Miguel Vela, Alba Gurt, Jaime Barceló-Vidal, Fabiola Ojeda, Iago López, Laura Gómez-García, María Isabel Loza, Elena Martín-García, Rafael Maldonado, Jordi Monfort
Journal: Journal of Infection (2023)
Association between public health policies on alcohol and worldwide cancer, liver disease and cardiovascular disease outcomes
Authors: Luis Antonio Díaz, Eduardo Fuentes-López, Francisco Idalsoaga, Gustavo Ayares, Oscar Corsi, Jorge Arnold, Macarena Cannistra, Danae Vio, Andrea Márquez-Lomas, Carolina Ramirez-Cadiz, María Paz Medel, María Hernandez-Tejero, Catterina Ferreccio, Mariana Lazo, Juan Pablo Roblero, Thomas G. Cotter, Anand V. Kulkarni, Won Kim, Mayur Brahmania, Alexandre Louvet, Elliot B. Tapper, Winston Dunn, Douglas Simonetto, Vijay H. Shah, Patrick S. Kamath, Jeffrey V. Lazarus, Ashwani K. Singal, Ramon Bataller, Marco Arrese, Juan Pablo Arab
Journal: Journal of Hepatology (2023)

The long-term impact of alcohol-related public health policies (PHPs) on disease burden is unclear. We aimed to assess the association between alcohol-related PHPs and alcohol-related health consequences.
Unraveling the potential clinical utility of circulating tumor DNA detection in colorectal cancer—evaluation in a nationwide Danish cohort
Authors: T.V. Henriksen, C. Demuth, A. Frydendahl, J. Nors, M. Nesic, M.H. Rasmussen, T. Reinert, O.H. Larsen, C. Jaensch, U.S. Løve, P.V. Andersen, T. Kolbro, O. Thorlacius-Ussing, A. Monti, M. Gögenur, J. Kildsig, P. Bondeven, N.H. Schlesinger, L.H. Iversen, K.A. Gotschalck, C.L. Andersen
Journal: Annals of Oncology (2023)

BackgroundIncreasingly, circulating tumor DNA (ctDNA) is proposed as a tool for minimal residual disease (MRD) assessment. Digital PCR (dPCR) offers low analysis costs and turnaround times of less than a day, making it ripe for clinical implementation. Here, we used tumor-informed dPCR for ctDNA detection in a large CRC cohort to evaluate the potential for postoperative risk assessment and serial monitoring, and how the metastatic site may impact ctDNA detection. Additionally, we assessed how altering the ctDNA calling algorithm could customize performance for different clinical settings.Patients and methodsStage II-III CRC patients (N=851) treated with curative intent were recruited. Based on whole exome sequencing on matched tumor and germline DNA, a mutational target was selected for dPCR analysis. Plasma samples (8mL) were collected within 60 days post operation and – for a patient subset (n=246) – every 3-4 months for up to 36 months. Single-target dPCR was used for ctDNA detection.ResultsBoth postoperative and serial ctDNA detection was prognostic of recurrence (HR=11.3, 95%CI 7.8-16.4, P<0.001; HR=30.7, 95%CI 20.2-46.7, P<0.001), with a cumulative ctDNA detection rate of 87% at the end of sample collection in recurrence patients. The ctDNA growth rate was prognostic of survival (HR=2.6, 95%CI 1.5-4.4, P=0.001). In recurrence patients, postoperative ctDNA detection was challenging for lung metastases (4/21 detected) and peritoneal metastases (2/10 detected). By modifying the cutoff for calling a sample ctDNA positive, we were able to adjust the sensitivity and specificity of our test for different clinical contexts.ConclusionsThe presented results from 851 stage II-III CRC patients demonstrate that our personalized dPCR approach effectively detects MRD post operation and shows promise for serial ctDNA detection for recurrence surveillance. The ability to adjust sensitivity and specificity shows exciting potential to customize the ctDNA caller for specific clinical settings.
Circulating tumor DNA and radiological tumor volume identify patients at risk for relapse with resected, early-stage non-small-cell lung cancer
Authors: H.T. Tran, S. Heeke, S. Sujit, N. Vokes, J. Zhang, M. Aminu, V.K. Lam, A. Vaporciyan, S.G. Swisher, M.C.B. Godoy, T. Cascone, B. Sepesi, D.L. Gibbons, J. Wu, J.V. Heymach
Journal: Annals of Oncology (2023)

BackgroundPredicting relapse and overall survival in early-stage non-small cell lung cancer (NSCLC) patients remains challenging. Therefore, we hypothesized that detection of circulating tumor DNA (ctDNA) can identify patients with increased risk of relapse and that integrating radiological tumor volume measurement along with ctDNA detectability improves prediction of outcome.Patients and MethodWe analyzed 366 serial plasma samples from 85 patients who underwent surgical resections and assessed ctDNA using a next-generation sequencing liquid biopsy assay, and measured tumor volume using CT-based 3-dimensional annotation.ResultsOur results showed that patients with detectable ctDNA at baseline or after treatment and patients who did not clear ctDNA after treatment had a significantly worse clinical outcome. Integrating radiological analysis allowed the stratification in risk groups prognostic of clinical outcome as confirmed in an independent cohort of 32 patients.ConclusionsOur findings suggest ctDNA and radiological monitoring could be valuable tools for guiding follow-up care and treatment decisions for early-stage NSCLC patients.